DSPAM is a scalable and open-source content-based spam filter designed for multi-user enterprise systems. On a properly configured system, many users experience results between 99.5% - 99.95%, or one error for every 200 to 2000 messages. DSPAM supports many different MTAs and can also be deployed as a stand-alone SMTP appliance. For developers, the DSPAM core engine (libdspam) can be easily incorporated directly into applications for drop-in filtering.

The DSPAM project has been implemented on many large and small scale systems with the largest being reported at about 350,000 mailboxes.

DSPAM is an adaptive filter which means it is capable of learning and adapting to each user's email. Instead of working off of a list of "rules" to identify spam, DSPAM's probabilistic engine examines the content of each message and learns what type of content the user deems as spam (or nonspam). This approach to machine-learning provides much higher levels of accuracy than commercial "hodge-podge" solutions, and with minimal resources. DSPAM's best recorded levels of accuracy have included 99.991% by one avid user (2 errors in 22,786) and 99.987% by the author (1 error in 7000), which could be ten times more accurate than a human being!